Covid 19 - City of Buenos Aires¶

1.1.1 Study introduction¶

The City of Buenos Aires published the vaccionation stats regarding the firs cuarter of the year 2021. The data shows all the people in the city area, that has received the COVID-19 Vaccine during 14JAN0201 and 14APR2021, in public and private institutions.

The variables created for the analysis of this view are the following:

Standard Variables:

  • FECHA_ADMINISTRACION: the date when the vaccine dosis has been aplied.
  • GRUPO_ETARIO: the pacients range of age (ordered in categories ranged by 9 years after 30 years old).
  • GENERO: gender of the person who receibed the vaccine.
  • VACUNA: vaccine brand applied.
  • TIPO_EFECTOR: divided bettwen Private and Public, means the institution that applied the dosis.
  • DOSIS_1: 1st of the 3 dosis permited.
  • DOSIS_2: 2do dosis.
  • ID_CARGA: loaded as NaN.
  • DOSIS_3: 3er dosis.
In [251]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
from matplotlib.cm import get_cmap
from matplotlib import cm

import plotly.io as pio
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go
from plotly.subplots import make_subplots
pio.renderers.default = 'notebook'

import warnings
warnings.filterwarnings('ignore')

import math

pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 1000)
In [252]:
df = pd.read_csv('dataset_total_vacunas.csv')
In [253]:
df.head()
Out[253]:
FECHA_ADMINISTRACION GRUPO_ETARIO GENERO VACUNA TIPO_EFECTOR DOSIS_1 DOSIS_2 ID_CARGA DOSIS_3
0 14JAN2021:00:00:00 41 a 50 M Sputnik Público 138 0 NaN 0.0
1 14JAN2021:00:00:00 41 a 50 M Sputnik Público nacional 28 0 NaN 0.0
2 14JAN2021:00:00:00 51 a 60 F Sputnik Privado 54 0 NaN 0.0
3 14JAN2021:00:00:00 51 a 60 F Sputnik Público 173 0 NaN 0.0
4 14JAN2021:00:00:00 51 a 60 F Sputnik Público nacional 27 0 NaN 0.0
In [254]:
table_count = 1
fig_count = 1

Over 38.531 cases, the data is divided between age ranges starting at less than 30 years, and going up in 9 years ranges.

What age category was vaccinated the most?¶

In [255]:
df_resumen = df.groupby('GRUPO_ETARIO').agg({'VACUNA': pd.Series.count}).reset_index()
df_resumen.rename(columns={'VACUNA': 'Q - Vacunas'}, inplace=True)

display(df_resumen.style.set_caption(f'Table{table_count}. Number of People percibing Vaccination'))
table_count
Table1. Number of People percibing Vaccination
  GRUPO_ETARIO Q - Vacunas
0 30 o menos 6244
1 31 a 40 5443
2 41 a 50 5248
3 51 a 60 5182
4 61 a 70 4959
5 71 a 80 4583
6 81 a 90 3889
7 91 o mas 2983
Out[255]:
1
In [256]:
fig = px.pie(df_resumen, values='Q - Vacunas', names='GRUPO_ETARIO', color_discrete_sequence=px.colors.sequential.Blues_r)
fig.update_layout(width=900,
                  height=600,
                  title='Figure 1. Category Age Distribution' )
# ethn = df[df.Ethnicity.isna()]['PatientID'].nunique() / df.PatientID.nunique()
# print(f"In {(1-ethn):.2%} of cases the ethnicity was mentioned")
fig.show()

Figure 1 shows the porcentage of people vaccinated during the period of analysis clustered by age range

What Vaccine brand was more applied in the Buenos Aires city area?¶

In [257]:
g = df_res
d = ["Q 1er Dosis",'Q 2da Dosis','Q 3ra Dosis']
fig = px.bar(g, x="VACUNA", y= d, color_discrete_sequence=px.colors.sequential.Blues_r)
fig.update_layout(
    xaxis_title="",
    yaxis_title="Applied Dosis",
    width=800,
    height=600,
    title='Figure 2. Dosis distribution by Vaccine Brand'
).update_xaxes(categoryorder="total descending")
fig.show()

What was the distributtion by gender between different vaccines brands?¶

In [258]:
df_dos = df
df_dos['VACUNA'] = df['VACUNA'].replace(['vacuna Coronavirus (CANSINO), vial x 1 dosis',
                                             'vacuna Coronavirus (PFIZER) 1ra - 2da dosis, vial x 6 dosis (0.3ml)',
                                             'vacuna Coronavirus (PFIZER) pediátrica, vial x 10 dosis (0.2ml)'],
                                            ['CANSINO','PFIZER','PFIZER (Pediatrica)'])
In [259]:
df_dos['Total_dosis'] = df[['DOSIS_1', 'DOSIS_2', 'DOSIS_3']].astype(float).sum(1)
df_dos
Out[259]:
FECHA_ADMINISTRACION GRUPO_ETARIO GENERO VACUNA TIPO_EFECTOR DOSIS_1 DOSIS_2 ID_CARGA DOSIS_3 Total_dosis
0 14JAN2021:00:00:00 41 a 50 M Sputnik Público 138 0 NaN 0.0 138.0
1 14JAN2021:00:00:00 41 a 50 M Sputnik Público nacional 28 0 NaN 0.0 28.0
2 14JAN2021:00:00:00 51 a 60 F Sputnik Privado 54 0 NaN 0.0 54.0
3 14JAN2021:00:00:00 51 a 60 F Sputnik Público 173 0 NaN 0.0 173.0
4 14JAN2021:00:00:00 51 a 60 F Sputnik Público nacional 27 0 NaN 0.0 27.0
... ... ... ... ... ... ... ... ... ... ...
38526 03MAR2022:00:00:00 91 o mas M PFIZER Público 0 2 NaN 3.0 5.0
38527 14APR2022:00:00:00 30 o menos F Moderna Público 3 5 NaN 46.0 54.0
38528 14APR2022:00:00:00 30 o menos F Sinopharm Público 35 60 NaN 5.0 100.0
38529 14APR2022:00:00:00 30 o menos F Sputnik Público 0 6 NaN 5.0 11.0
38530 14APR2022:00:00:00 30 o menos F PFIZER Público 13 20 NaN 125.0 158.0

38531 rows × 10 columns

In [260]:
g =  df_dos.groupby(['VACUNA','GENERO']).agg({'Total_dosis': pd.Series.sum}).reset_index() 
fig = px.bar(g, x='Total_dosis', y='VACUNA',color='GENERO',text='Total_dosis',color_discrete_sequence=px.colors.sequential.Blues_r)
fig.update_layout(
    xaxis_title='',
    yaxis_title='Vaccine Brand',
    width=1000,
    height=800,
    title='Figure 3. Vaccine Brand by Gender - Sum of 3 dosis'
).update_yaxes(categoryorder="total ascending")
fig.show()

How was the campaing vaccine distribution between Public and Private stablishments?¶

In [261]:
d =  df_dos.groupby(['VACUNA','TIPO_EFECTOR']).agg({'Total_dosis': pd.Series.sum}).reset_index() 
fig = px.bar(d, y='Total_dosis', x='VACUNA',color='TIPO_EFECTOR',text='Total_dosis', color_discrete_sequence=px.colors.sequential.Blues_r)
fig.update_layout(
    xaxis_title='Vaccine Brand',
    yaxis_title='Q of Vacciones',
    width=1000,
    height=800,
    title='Figure 4. Vaccine Brand by efector kind - Sum of 3 dosis'
).update_xaxes(categoryorder="total ascending")
fig.show()
In [262]:
df_resumen_dos = df_dos.groupby('TIPO_EFECTOR').agg({'Total_dosis': pd.Series.sum}).reset_index()
df_resumen_dos.rename(columns={'TIPO_EFECTOR': 'Tipo Organismo'}, inplace=True)
# df_resumen_dos['Dosis_Totales'] = df_resumen_dos['Total_dosis']
# df_resumen_dos['Dosis_Totales'] = df_resumen_dos['Dosis_Totales'].map('{:,.2f}'.format)
# df_resumen_dos = df_resumen_dos.drop('Total_dosis',axis=1)
display(df_resumen_dos.style.set_caption(f'Table{table_count}. Number of People percibing Vaccination'))
table_count
Table1. Number of People percibing Vaccination
  Tipo Organismo Total_dosis
0 Privado 177318.000000
1 Público 7736928.000000
2 Público nacional 21701.000000
Out[262]:
1
In [263]:
fig = px.pie(df_resumen_dos, values='Total_dosis', names='Tipo Organismo', color_discrete_sequence=px.colors.sequential.Blues_r)
fig.update_layout(width=900,
                  height=600,
                  title='Figure 5. Sort of Facility (Pirvate - Public)' )
# ethn = df[df.Ethnicity.isna()]['PatientID'].nunique() / df.PatientID.nunique()
# print(f"In {(1-ethn):.2%} of cases the ethnicity was mentioned")
fig.show()

As the pie shows, 97.5% of the inoculations were given at Public facilities, when only 2.23% at private, the 0.273% is refered as 'Publico Nacional' being a mix of public and co-owned by the Buenos Aires city hall and the national goverment

Wich months were applied most of the vaccines?¶

In [268]:
df_dos['Fecha'] =  pd.to_datetime(df_dos['FECHA_ADMINISTRACION'], format='%d%b%Y:%H:%M:%S')
df_dos['Mes'] =  df_dos['Fecha'].dt.month
# df_dos['Fecha'] = df_dos['FECHA_ADMINISTRACION'].str[:9]
# df_dos
df_dos['Mes'].replace(to_replace=[1,2,3,4,5,6,7,8,9,10,11,12],value=['Ene','Feb','Mar','Abr','May',
                                                                     'Jun','Jul','Ago','Sep','Oct','Nov','Dic'
                                                                    ],inplace=True)
df_dos
Out[268]:
FECHA_ADMINISTRACION GRUPO_ETARIO GENERO VACUNA TIPO_EFECTOR DOSIS_1 DOSIS_2 ID_CARGA DOSIS_3 Total_dosis Fecha Mes
0 14JAN2021:00:00:00 41 a 50 M Sputnik Público 138 0 NaN 0.0 138.0 2021-01-14 Ene
1 14JAN2021:00:00:00 41 a 50 M Sputnik Público nacional 28 0 NaN 0.0 28.0 2021-01-14 Ene
2 14JAN2021:00:00:00 51 a 60 F Sputnik Privado 54 0 NaN 0.0 54.0 2021-01-14 Ene
3 14JAN2021:00:00:00 51 a 60 F Sputnik Público 173 0 NaN 0.0 173.0 2021-01-14 Ene
4 14JAN2021:00:00:00 51 a 60 F Sputnik Público nacional 27 0 NaN 0.0 27.0 2021-01-14 Ene
... ... ... ... ... ... ... ... ... ... ... ... ...
38526 03MAR2022:00:00:00 91 o mas M PFIZER Público 0 2 NaN 3.0 5.0 2022-03-03 Mar
38527 14APR2022:00:00:00 30 o menos F Moderna Público 3 5 NaN 46.0 54.0 2022-04-14 Abr
38528 14APR2022:00:00:00 30 o menos F Sinopharm Público 35 60 NaN 5.0 100.0 2022-04-14 Abr
38529 14APR2022:00:00:00 30 o menos F Sputnik Público 0 6 NaN 5.0 11.0 2022-04-14 Abr
38530 14APR2022:00:00:00 30 o menos F PFIZER Público 13 20 NaN 125.0 158.0 2022-04-14 Abr

38531 rows × 12 columns

In [269]:
# Histogram
g = df_dos.groupby(['Total_dosis', 'VACUNA'])['Mes'].max().reset_index()
fig = px.histogram(g, x='Mes', color='VACUNA', color_discrete_sequence=px.colors.sequential.Blues_r)

fig.update_layout(
    xaxis_title="Age",
    yaxis_title="Number of Patients",
    title="Figure 6. Distribution of Vaccines During the year",
    width = 900,
    height = 600
).update_xaxes(categoryorder="trace")
fig.show()
In [ ]:
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